CN115760595A - Ultra-wide-angle lens photo distortion correction method based on line segment characteristics - Google Patents

Ultra-wide-angle lens photo distortion correction method based on line segment characteristics Download PDF

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CN115760595A
CN115760595A CN202211314646.8A CN202211314646A CN115760595A CN 115760595 A CN115760595 A CN 115760595A CN 202211314646 A CN202211314646 A CN 202211314646A CN 115760595 A CN115760595 A CN 115760595A
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line segment
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wide
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CN115760595B (en
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常晓宇
王敏
王港
郭争强
刘宇
张晓男
谢鑫浩
孙方德
朱进
陈金勇
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CETC 54 Research Institute
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Abstract

The invention discloses a line segment characteristic-based ultra-wide-angle lens photo distortion correction method, and belongs to the technical field of image processing. Which comprises the following steps: extracting line segments from the super-wide-angle lens image to obtain a line segment set; dividing the whole image into a plurality of areas; respectively defining upper, lower, left and right edge regions of an image, and only correcting linear distortion in the regions; respectively acquiring a line segment set positioned in each edge area; dividing the line segment into a plurality of line segment subsets at equal intervals according to the inclination angles of the line segments in the edge region line segment set; searching line segment pairs in the line segment subset to form a matched line segment set; generating a line segment characteristic correction point set; forming a global correction matching point set; and establishing a quadratic polynomial correction model, and correcting the ultra-wide-angle lens image by a least square method. The invention fully utilizes the linear characteristics of the target in the image and completes the image correction by combining the image resampling method, thereby effectively reducing the distortion of the ultra-wide-angle lens of the mobile phone in a scene with the linear target.

Description

Ultra-wide-angle lens photo distortion correction method based on line segment characteristics
Technical Field
The invention belongs to the technical field of image processing, and particularly relates to a method for correcting distortion of a super-wide-angle lens photo based on line segment characteristics.
Background
With the rapid development of smart phones, mobile phone photography gradually enters mass life and becomes a main photographing mode in daily application, so that the convenience of mass photography is greatly improved, and the photographing cost is reduced. In recent years, the ultra-wide-angle lens is gradually applied to smart phones, can bring a larger visual angle and a wider visual field for mobile phone photos, and meanwhile, has a large depth of field for taking photos, and can accommodate more close-range scenes and far-range scenes.
Currently, the maximum viewing angle of the super-wide angle camera of many mobile phones exceeds 100 degrees, and more particularly reaches 150 degrees. Although a super wide-angle lens can capture a larger scene, the resulting picture may have larger distortion, especially near the edge. Most mobile phones can carry out fixed parameter calibration on the ultra-wide-angle lens when leaving a factory, and still have distortion to a certain degree due to the difference of distance of a target in a view field, particularly when the target with obvious linear characteristics is shot. When the ultra-wide-angle photo has an object with obvious linear characteristics, the image can be corrected directly according to the linear characteristics. Therefore, how to correct the image by using the linear characteristics of the super-wide-angle lens photo becomes an urgent problem to be solved.
Disclosure of Invention
The invention aims to provide a line segment characteristic-based ultra-wide-angle lens photo distortion correction method, which fully utilizes the linear characteristics of a target in an image and completes image correction by combining an image resampling method, thereby effectively reducing the distortion of a mobile phone ultra-wide-angle lens in a scene with a linear target.
The technical scheme adopted by the invention is as follows:
a method for correcting the distortion of a super-wide-angle lens photo based on line segment characteristics comprises the following steps:
step 1, extracting line segments from an ultra-wide-angle lens image IM by adopting an LSD algorithm to obtain a line segment set L set ；
Step 2, dividing the whole super wide-angle lens image IM into 4 regions with the number of the regions being 4 x 4, and recording the region of the ith row and the jth column as Z ij ；
Step 3, respectively defining upper, lower, left and right edge regions E of the image top 、E bot 、E lef And E rig Correcting only the linear distortion in the region;
step 4, respectively obtaining the line segment sets L positioned in each edge area through the position relation E ；
Step 5, using the edge region line segment set L E Based on the inclination angle theta of the middle line segment, and dividing L according to the interval d theta E Equal-spacing division into multiple line segment subsets
Figure BDA0003908616320000011
Step 6, according to the inclination angle and distance constraint of the line segment, on the line segment subset
Figure BDA0003908616320000021
Finding line segment pair to form matched line segment set
Figure BDA0003908616320000022
Step 7, based on the obtained matching line segment set
Figure BDA0003908616320000023
Generating a set of line segment feature correction points P line ；
Step 8, in each sub-area Z of the super wide-angle lens image IM ij Inner uniform selection points to form a global correction matching point set P adj ；
Step 9, correcting the matching point set P according to the universe adj Establishing a quadratic polynomial correction model, and correcting the super wide-angle lens image IM by a least square method to obtain a corrected image IM adj 。
Further, in step 4, L is judged according to the following conditions set Whether the line segment L in (A) belongs to the corresponding line segment set L E ：
Figure BDA0003908616320000024
E∈{E top ,E bot ,E lef ,E rig }
Where N () denotes the number of pixels of the line segment and δ denotes a scale threshold.
Further, the line segment subset in step 5
Figure BDA0003908616320000025
The calculation method of (c) is as follows:
Figure BDA0003908616320000026
Figure BDA0003908616320000027
wherein int () is rounded up, θ l Indicates the inclination angle, theta, of the line segment l min And theta max Respectively a subset of line segments
Figure BDA0003908616320000028
Minimum and maximum values of the pitch of the neutral line.
Further, in step 6, if the line segment subset
Figure BDA0003908616320000029
Two line segments l in i And l j If the following conditions are satisfied, it is considered that the segment pair (l) is formed by the segment pairs i ,l j ) Belonging to a set of matching line segments
Figure BDA00039086163200000210
Figure BDA00039086163200000211
Where | x | represents the absolute value of θ thr A threshold value representing a difference in inclination angle of the line segment, | represents the length of the line segment, dis () represents the distance between two points, P M The midpoint of the corresponding line segment is indicated, and λ represents the line segment position constraint threshold.
Further, the specific manner of step 7 is as follows:
for a set of matched line segments
Figure BDA0003908616320000031
Segment pair (l) in (1) i ,l j ) Taking the line segment closer to the boundary of the edge region E as the line segment l to be corrected a Let l a Respectively as a starting point and an end point of
Figure BDA0003908616320000032
And
Figure BDA0003908616320000033
then the end point of the line segment to be corrected
Figure BDA0003908616320000034
The calculation of (c) is as follows:
Figure BDA0003908616320000035
Figure BDA0003908616320000036
Figure BDA0003908616320000037
in the formula I a The representation is composed of line segment pairs (l) i ,l j ) The generated line segment, | l to be corrected a I represents l a The length of the line segment of (a),
Figure BDA0003908616320000038
the absolute value of the inclination angle difference is obtained;
there are c sets P of line segment feature correction points line Expressed as:
Figure BDA0003908616320000039
in the formula (I), the compound is shown in the specification,
Figure BDA00039086163200000310
the pairs of correction points produced by the line segments are indicated,
Figure BDA00039086163200000311
in order to correct the position of the front point,
Figure BDA00039086163200000312
the corrected position of the point.
Further, in step 8, the matching point set P is corrected in the whole domain adj Set of non-line segment region points P noline And line segment characteristic correction point set P line Consists of the following components:
P adj ＝P noline +P line
Figure BDA00039086163200000313
Figure BDA00039086163200000314
in the formula (I), the compound is shown in the specification,
Figure BDA00039086163200000315
is a region Z m Middle point of (Z) m Set of finger and edge line segments L E The disjoint areas are composed of partial edge areas and central areas.
The invention has the following beneficial effects:
(1) The invention provides a line segment characteristic-based ultra-wide-angle lens photo distortion correction method, which can effectively correct an ultra-wide-angle camera image with straight lines at the edge and reduce the distortion of the central area of the image.
(2) The method can effectively extract the linear characteristics, automatically search the matching points to be corrected and provide technical support for automatic image correction.
Drawings
FIG. 1 is a schematic diagram of image region division;
fig. 2 is a schematic diagram illustrating the principle of an ultra-wide angle camera image correction method.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
As shown in fig. 2, a method for correcting distortion of a super-wide-angle lens photograph based on line segment characteristics includes the following steps:
step 1, adopting an LSD algorithm to extract line segments from an ultra-wide-angle lens image IM to obtain a line segment set L set . When there are n line segments, it can be expressed as:
L set ＝{l 1 ,l 2 ,l 3 ...l n }
step 2, dividing the whole image into 4-4 areas, and recording each area as Z ij . In this example, the whole image is divided into 16 regions, the positions and numbers of the regions are shown in FIG. 1, Z ij Can be expressed as:
Z ij ＝{Z 11 ,Z 12 ,...,Z 44 }
step 3, respectively defining upper, lower, left and right edge regions E of the image top 、E bot 、E lef And E rig Only the linear distortion in the region is corrected, and the defined edge region E is specifically expressed as follows:
E top ＝{Z 11 ,Z 12 ,Z 13 ,Z 14 }
E bot ＝{Z 41 ,Z 42 ,Z 43 ,Z 44 }
E lef ＝{Z 11 ,Z 21 ,Z 31 ,Z 41 }
E rig ＝{Z 14 ,Z 24 ,Z 34 ,Z 44 }
step 4, respectively obtaining the line segment sets L positioned in each edge area through the position relation E . Calculating only the line segments in the edge region, and judging L set The condition that the line segment l in (1) belongs to the edge region E is as follows:
Figure BDA0003908616320000041
E∈{E top ,E bot ,E lef ,E rig }
where N () denotes the number of pixels of the line segment and δ denotes a scale threshold.
Step 5, using the edge region line segment set L E Dividing the middle line segment into a plurality of line segment subsets at equal intervals according to the inclination angle theta of the middle line segment
Figure BDA0003908616320000042
Division of line segments l into line segment subsets
Figure BDA0003908616320000043
The conditions of (a) are as follows:
Figure BDA0003908616320000044
Figure BDA0003908616320000051
wherein int () is rounded up, θ l Indicates the inclination angle, theta, of the line segment l min And theta max Respectively a subset of line segments
Figure BDA0003908616320000052
Minimum and maximum values of the pitch of the median line.
Step 6, according to the inclination angle and distance constraint of the line segment, on the line segment subset
Figure BDA0003908616320000053
Finding line segment pair to form matched line segment set
Figure BDA0003908616320000054
Line segment subset
Figure BDA0003908616320000055
Two line segments l in i And l j If the following conditions are satisfied, it is considered that the segment pair (l) is composed of the same i ,l j ) Belonging to a set of matched line segments
Figure BDA0003908616320000056
The constraints are as follows:
Figure BDA0003908616320000057
where | x | represents the absolute value of θ thr A threshold value representing a difference in inclination angle of the line segment, | represents the length of the line segment, dis () represents the distance between two points, P l M The midpoint of the line segment l is indicated and λ represents the line segment position constraint threshold.
Step 7, based on the obtained matching line segment set
Figure BDA0003908616320000058
Generating a set of line segment feature correction points P line . For matched segment pairs (l) i ,l j ) The line segment closer to the boundary of the edge region E is the line segment l to be corrected a Let l a Respectively as a starting point and an end point of
Figure BDA0003908616320000059
And
Figure BDA00039086163200000510
the position of the corrected point corresponding to the end point of the line segment to be corrected
Figure BDA00039086163200000511
The calculation method of (c) is as follows:
Figure BDA00039086163200000512
Figure BDA00039086163200000513
Figure BDA00039086163200000514
in the formula I a Is represented by a line segment pair (l) i ,l j ) The generated line segment to be corrected, | | l a I represents l a The length of the line segment of (a),
Figure BDA00039086163200000515
the absolute value of the difference in the tilt angle is shown.
Therefore, when there are c sets P of line segment correction points line Can be expressed as:
Figure BDA00039086163200000516
in the formula (I), the compound is shown in the specification,
Figure BDA00039086163200000517
the pairs of correction points produced by the line segments are indicated,
Figure BDA00039086163200000518
in order to correct the position of the front point,
Figure BDA00039086163200000519
the corrected position of the point.
Step 8, uniformly selecting points in each sub-region Z of the image to form a global correction matching point set P adj 。P adj Set of non-line segment region points P noline And line segment characteristic correction point set P line Consists of the following components:
P adj ＝P noline +P line
Figure BDA0003908616320000061
Figure BDA0003908616320000062
in the formula (I), the compound is shown in the specification,
Figure BDA0003908616320000063
is a region Z m Middle point of (Z) m Refers to and edge line segment set L E The disjoint areas are mainly composed of partial edge areas and central areas.
Step 9, correcting the matching point set P according to the universe adj Establishing a quadratic polynomial correction model, correcting the super wide-angle lens image by a least square method to obtain a corrected image IM adj . When the global correction matches the point set P adj When the point is formed by r points, the method can be expressed as follows:
P adj ＝{[(μ 1 ,v 1 ),(x 1 ,y 1 )],...,[(μ r ,v r ),(x r ,y r )]}
wherein (mu) r ,v r ) And (x) r ,y r ) And respectively correcting the pre-correction position and the post-correction position in the r-th pair of matching points.
Then use the correct matching point set P adj The constructed quadratic polynomial expression is as follows:
Figure BDA0003908616320000064
in the formula, b 00 ,b 01 ,b 02 ,b 11 ,b 12 ,b 22 And e 00 ,e 01 ,e 02 ,e 11 ,e 12 ,e 22 The model coefficients are respectively, and the solution can be carried out through the polynomial.
The final constructed model can be expressed as:
Figure BDA0003908616320000065
in the formula, (x, y) represents an image IM after correction of the ultra-wide-angle lens adj Point (μ, v) represents a point in the ultra-wide-angle lens image IM to be corrected.
In a word, the ultra-wide angle distortion image correction algorithm provided by the invention is oriented to the situation that the image edge has a straight line and can generate large distortion, the line segment to be corrected is judged through the line segment characteristics, the matching point is automatically extracted, manual intervention and identification are not needed, and important technical support is provided for convenient, rapid and efficient post-processing of the ultra-wide angle lens image.

Claims (6)

1. A super wide-angle lens photo distortion correction method based on line segment characteristics is characterized by comprising the following steps:
step 1, extracting line segments from an ultra-wide-angle lens image IM by adopting an LSD algorithm to obtain a line segment set L set ；
Step 2, dividing the whole super wide-angle lens image IM into 4 regions with the number of the regions being 4 x 4, and recording the region of the ith row and the jth column as Z ij ；
Step 3, respectively definingUpper, lower, left and right edge regions E of the image top 、E bot 、E lef And E rig Correcting only the linear distortion in the region;
step 4, respectively obtaining the line segment sets L positioned in each edge area through the position relation E ；
Step 5, using the edge region line segment set L E Based on the inclination angle theta of the middle line segment, L is divided by the interval d theta E Equidistant division into a plurality of line segment subsets
Figure FDA0003908616310000011
Step 6, according to the inclination angle and distance constraint of the line segment, on the line segment subset
Figure FDA0003908616310000012
Finding line segment pair to form matched line segment set
Figure FDA0003908616310000013
Step 7, based on the obtained matching line segment set
Figure FDA0003908616310000014
Generating a set of line segment feature correction points P line ；
Step 8, in each sub-area Z of the super wide-angle lens image IM ij Internal uniform selection of points to form a global calibration matching point set P adj ；
Step 9, correcting the matching point set P according to the universe adj Establishing a quadratic polynomial correction model, and correcting the super wide-angle lens image IM by a least square method to obtain a corrected image IM adj 。
2. The method for correcting the distortion of the ultra-wide-angle lens photograph based on the line segment characteristics as claimed in claim 1, wherein in the step 4, L is determined according to the following condition set Whether the line segment L in (A) belongs to the corresponding line segment set L E ：
l∈L E if
Figure FDA0003908616310000015
E∈{E top ,E bot ,E lef ,E rig }
Where N () denotes the number of pixels of a line segment and δ denotes a scaling threshold.
3. The method as claimed in claim 2, wherein the line segment subset in step 5 is a line segment subset
Figure FDA0003908616310000016
The calculation method of (c) is as follows:
Figure FDA0003908616310000017
if θ min +dθ×(k-1)≤θ l ＜θ min +dθ×k
Figure FDA0003908616310000018
wherein int () is rounded up, θ l Indicates the inclination angle, theta, of the line segment l min And theta max Respectively a subset of line segments
Figure FDA0003908616310000019
Minimum and maximum values of the pitch of the median line.
4. The method as claimed in claim 3, wherein in step 6, if the line segment subset is selected, the line segment is selected from the group consisting of a plurality of line segments
Figure FDA0003908616310000021
Two line segments l in i And l j A pair of line segments (A) and (B) satisfying the following conditionl i ,l j ) Belonging to a set of matched line segments
Figure FDA0003908616310000022
Figure FDA0003908616310000023
Where | x | represents the absolute value of θ thr A threshold value representing a difference in inclination angle of the line segment, | represents the length of the line segment, dis () represents the distance between two points, P M The midpoint of the corresponding line segment is indicated, and λ represents the line segment position constraint threshold.
5. The method for correcting the distortion of the photograph of the ultra-wide-angle lens based on the line segment characteristics as claimed in claim 4, wherein the specific mode of the step 7 is as follows:
for a set of matched line segments
Figure FDA0003908616310000024
Segment pair (l) in (1) i ,l j ) Taking the line segment closer to the boundary of the edge region E as the line segment l to be corrected a Let l a Respectively as a starting point and an end point of
Figure FDA0003908616310000025
And
Figure FDA0003908616310000026
then the end point of the line segment to be corrected
Figure FDA0003908616310000027
The calculation method of (c) is as follows:
Figure FDA0003908616310000028
Figure FDA0003908616310000029
Figure FDA00039086163100000210
in the formula I a The representation is composed of line segment pairs (l) i ,l j ) The generated line segment to be corrected, | | l a I represents l a The length of the line segment of (a),
Figure FDA00039086163100000211
the absolute value of the inclination angle difference is obtained;
there are c sets P of line segment feature correction points line Expressed as:
Figure FDA00039086163100000212
in the formula (I), the compound is shown in the specification,
Figure FDA00039086163100000213
the pairs of correction points produced by the line segments are indicated,
Figure FDA00039086163100000214
in order to correct the position of the front point,
Figure FDA00039086163100000215
the corrected position of the point.
6. The method for correcting distortion of ultra-wide-angle lens photograph based on line segment characteristics as claimed in claim 5, wherein in step 8, the global correction matching point set P adj Set of non-line segment region points P noline And line segment characteristic correction point set P line Consists of the following components:
P adj ＝P noline +P line
Figure FDA0003908616310000031
Figure FDA0003908616310000032
in the formula (I), the compound is shown in the specification,
Figure FDA0003908616310000033
is region Z m Midpoint of (c), Z m Set of finger and edge line segments L E The disjoint areas are composed of partial edge areas and central areas.
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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006330772A (en) * 2005-05-23 2006-12-07 Hitachi Zosen Corp Distortion correction method for photographic image
US20090088999A1 (en) * 2005-10-31 2009-04-02 Mitutoyo Corporation Optical aberration correction for machine vision inspection systems
CN104537663A (en) * 2014-12-26 2015-04-22 广东中科遥感技术有限公司 Method for rapid correction of image dithering
CN111667414A (en) * 2019-03-06 2020-09-15 厦门雅迅网络股份有限公司 Image distortion correction method and computer-readable storage medium

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2006330772A (en) * 2005-05-23 2006-12-07 Hitachi Zosen Corp Distortion correction method for photographic image
US20090088999A1 (en) * 2005-10-31 2009-04-02 Mitutoyo Corporation Optical aberration correction for machine vision inspection systems
CN104537663A (en) * 2014-12-26 2015-04-22 广东中科遥感技术有限公司 Method for rapid correction of image dithering
CN111667414A (en) * 2019-03-06 2020-09-15 厦门雅迅网络股份有限公司 Image distortion correction method and computer-readable storage medium

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
徐丁天等: "基于人眼跟踪的360°悬浮显示系统及其畸变校正方法", 光学学报, vol. 42, no. 9, pages 232 - 240 *
艾文宇: "光场相机建模与畸变校正改进方法研究", 中国优秀硕士学位论文全文数据库 (基础科学辑), pages 1 - 58 *

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